Energy management strategies, control systems, and artificial intelligence-based algorithms development for hydrogen fuel cell-powered vehicles: A review

• Main Authors: Oladosu T.L., Pasupuleti J., Kiong T.S., Koh S.P.J., Yusaf T.
• Other Authors: 57202498005
• Format: Review
• Published: Elsevier Ltd 2025
• Subjects: Control systems; Cost effectiveness; Energy management; Energy management systems; Fossil fuels; Hydrogen fuels; Reinforcement learning; Algorithms development; Carbon emissions; Fuel cell electric vehicle; Fuel-cell powered vehicles; Hybridisation; Hydrogen fuel cells; Management strategies; Multi objective; System strategies; Zero carbons; Fuel cells

Hydrogen fuel cell electric vehicles (HFCEVs) are gaining revived attention due to the HFCEVs promising potential as important syndicates to net zero carbon emission attainment. However, HFCEVs' performance and cost-effectiveness do not yet match up with battery electric vehicles (BEVs) and traditional fossil fuel vehicles despite many different Energy Management System (EMS) strategies previously adopted. Rule-based controls are still limited specifically in handling multi-objective systems as HFCEVs and some optimization-based algorithms also pose computational and retrofitting difficulties. Therefore, this study presents the prospect of artificial intelligence-based algorithms, control systems, and energy management strategies advances on HFCEVs performance optimization. EMS strategies; AI-based algorithms categories, functions and hybridization; the state-of-art and future direction of AI-based algorithms and HFCEVs? cost components amongst others are explained in the study. The multi-objective-based algorithm, reinforcement learning algorithm, and different hybridizations are enhancing HFCEVs cost-competing edge. ? 2024 Hydrogen Energy Publications LLC